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Will AI create a world where no job is needed? Will computers achieve human-level intelligence? Where will the dramatic OpenAI storyline end up? The drama unfolding in the world of AI has proven that readers and investors need to follow the space closely—and it's changing minute to minute. Predictions, ideas, and execution around the technology are, to put it mildly, all over the map. But one thing we know for sure about this sector, which is still in its infancy: Investors and companies large and small are taking it seriously. Microsoft—itself a major player via its relationship with OpenAI—recently found that for every $1 invested in AI, companies are reaping $3.50 in return. And according to Crunchbase, one in four dollars invested in American startups have gone to AI companies this year; already 200 AI unicorns have been created. But as anyone who has lived through a hype cycle knows, even the hottest sector mints far more miserable failures than stunning successes. For the inaugural Fortune 50 AI Innovators list we canvassed VCs, industry analysts, and our own formidable staff of AI experts to identify companies that are at the cutting edge. One thing we know for sure: The work these companies are doing won’t just shape the future of AI, it will shape the world we all live in.

Abnormal Security

When ChatGPT launched in November 2022, Evan Reiser’s first thought was how amazing the technology was. And then: “Oh, my God, the bad guys are going to get this too,” he recalls. Reiser cofounded Abnormal Security in 2018 with Sanjay Jeyakumar to help detect and prevent email cyberattacks. The company uses AI and machine learning for behavioral profiling, analyzing data from company platforms like Slack to help spot socially engineered email scams and determine whether messages were sent by an actual employee or by a hacker posing as one. Reiser says the explosion in popularity of ChatGPT and other chatbots has increased the sophistication of email attacks. But Abnormal is planning to be around for the long haul to combat them: Earlier this year, the firm integrated with workplace software including Slack, Zoom, and Microsoft Teams, and plans to add others, like Salesforce or ServiceNow. The company says its customers include 12% of Fortune 500 companies. And recently, the company announced it passed $100 million in annual recurring revenue. It’s also flush with funding: Abnormal has raised over $280 million from investors including Insight Partners, Greylock Partners, and Menlo Ventures, and is valued at $4 billion. Having teased IPO plans for years, Reiser says now the company is focusing on doing more to protect customers, and it could go public in a couple of years.

Absci

Absci founder and CEO Sean McCLain didn’t set out to build a drug-generating A.I. company in 2011. He set off to find a way to engineer proteins for more effective treatments. What he ended up building was the technology to gather protein-based data that large language models can use to predict the best drug for a biological problem. The wet labs can generate and test 3 million AI-generated drug designs in a six-week time frame. The company has created an immunology antibody (McClain declined to disclose the disease it will target) that it anticipates will be in use in 2025. Absci went public in 2021, with a share price of $16 that jumped to $28.48 within the week, valuing the company at more than $2 billion. But that would be its peak, as the share price dropped drastically over the following year and has stayed under $5 per share since May 2022. The company administered layoffs in August 2022 amid a restructuring.

Adept AI

Adept AI is among the companies creating the next generation of AI business assistants—AI that doesn’t just generate words, but can perform actions and analysis for executives using every piece of software on their desktop. The San Francisco-based startup counts among its co-founders former Google researchers David Luan, Niki Parmar and Ashish Vaswani, all AI royalty. Parmer and Vaswani, who were part of the Google team that invented the Transformer model on which the entire LLM revolution has been based, exited Adept in 2022 for another startup. But Adept is still on track to make a big splash. The company launched from stealth over a year ago and has raised $415 million total in VC funding.

Adobe

Adobe, the creative design and editing giant that brought in $17.61 billion in revenue during fiscal year 2022, has made a splash in the generative AI space. The company employs more than 29,000, and you might expect AI development to be sluggish as a result. But chief technology officer Ely Greenfield says that it mobilized quickly on years of data collection and research to launch Firefly in less than a year.

Two things set this tool apart. First, it is integrated into tools that creatives already use. Photoshop users can deploy Generative Fill to implement text-to-image edits, like changing the pattern of a shirt or dropping a new object into the photo. Second, the generated imagery is, the company says, safe for commercial use, with Adobe offering to idemnify users against any copyright infringement claims. Firefly is trained on the company’s vast catalog of Adobe Stock content, which it says it has the right to use for AI training (despite complaints from some Adobe Stock creators that they didn’t realize they were giving Adobe the right to do this when they uploaded imagery.) Adobe is eyeing integrating generative AI into more tools in the Creative Cloud.

In October the company unveiled Firefly Image 2, which brought quality upgrades to AI-generated images and allows users to pick specific styles that they want their generated image to resemble. The next frontier, said Greenfield, comes with artists’ acceptance of A.I. He sees a world in which artists could train A.I. on their own work and license it out.

Aligned AI

Aligned AI made a name for itself when its content moderation filter crushed OpenAI’s. It captured 97% of problematic responses compared to OpenAI’s 32%, Fortune’s Jeremy Kahn reported. Since then, it has become the first company to show that its AI model can master a challenging AI safety and alignment benchmark based on the simple video game environment CoinRun. Safety has been Aligned AI’s calling card from the get go. “You can actually think logically about danger from artificial intelligence, and create technical solutions that make progress on the problem,” says Rebecca Gorman, Aligned AI cofounder and CEO. Gorman and Stuart Armstrong started the Oxford, England-based startup in December 2021 with the idea that capabilities and safety in AI didn’t have to be a zero-sum game. Instead, they believe that the safer AI is, the more effective it will ultimately be, because end users will trust it more.

The company is still in the early stages of its development, with ￡600,000 (approximately $762,000) in pre-seed money and just six employees. It’s now ramping up hiring of technical roles as it looks to develop partnerships with AI industry players it hopes to provide with safety features. Aligned aims to partner with the major industry players for each use case of AI, like quality assurance or robtoics, Gorman says.

Aligned Research Center (ARC)

One of the few nonprofits on our list, the Alignment Research Center (ARC) was founded in June 2021 by former OpenAI employee Paul Christiano to study the “alignment problem”—the question of how to align AI’s actions with human values, a challenge that becomes more daunting as AI systems approach artificial general intelligence (or AGI), a single AI model that can perform all cognitive tasks a human can as well or better than we can. If AI starts to develop intentions of its own–even if these are just sub-goals needed to accomplish a human-specified primary goal–alignment becomes a pressing issue. ARC has worked with big AI houses like OpenAI and Anthropic to “red team” their models by seeing how the models respond to the kinds of prompts a malicious actor might use, and to see if the models themselves are capable of potentially dangerous self-direction actions, such as deception or self-replication. When OpenAI granted ARC access to its GPT-4 model before it was released to the public, ARC tested whether it could do things like hide itself on a server or lie to humans—no to the first, yes to the second. We can all sleep easier knowing ARC doesn’t think that present-day AI systems could pose the existential risks the most ardent “AI doomers” fear. But that may not remain true for much longer.

Anthropic

Who can rival OpenAI’s ubiquitous chatbot ChatGPT? A team of ex-OpenAI researchers are hoping the answer is Anthropic. Former OpenAI research executives including brother and sister Dario and Daniela Amodei founded Anthropic in 2021 to create the safest AI systems out there—ones that wouldn’t spew misinformation or harmful responses as some chatbots, including ChatGPT, have been known to do. Anthropic’s chatbot Claude, released in early 2023, has some notable differences from its peers: The company claims the chatbot is “l(fā)ess likely to produce harmful outputs,” and describes its aim as being helpful, harmless, and honest. The startup also recently expanded the number of words Claude can process in one query to 75,000—the length of many novels—which it says enables Claude to accurately analyze technical documents like financial filings or legal contracts. (OpenAI has recently expanded the number of words ChatGPT can process to counter Anthropic’s advantage.) Go-to-market lead Sandy Banerjee told Fortune that Anthropic has customers “in the thousands” using Claude, from Y Combinator startups to large public companies like Zoom. The company recently raised a whopping $4 billion from Amazon and is reportedly in talks to raise $2 billion more from Google. That is on top of $550 million the company had raised previously from Google and funding from other investors including Menlo Ventures and Salesforce Ventures.

Anyscale

What do OpenAI, Instacart, Netflix, Cohere, and Uber have in common? They all use Ray, the open-source software infrastructure framework that helps AI developers scale their networks, according to its maker, four-year-old startup Anyscale. In recent years, building and running AI models has become increasingly compute-intensive—meaning it can be difficult and time-consuming to distribute the compute workloads needed to train, tune, and run massive AI systems across clusters of servers. “It’s very common for people working in AI to spend 50% of their time setting up clusters of machines and configuring the resources,” Anyscale CEO and cofounder Robert Nishihara told Fortune. Ray is handling that infrastructure side of things, reducing the time to train and deploy AI models to minutes, they say. Ray was developed as an open-source project by a group of researchers at the UC Berkeley RISELab, and later became the main product of Anyscale, which is now funded by top Silicon Valley investors, including Andreessen Horowitz, NEA, Addition, and Intel Capital. As interest in building AI systems has skyrocketed over the past six months, so has demand for Ray, Nishihara says. And he says they’ve noticed a big trend: More regular developers with no machine learning expertise are wanting to build AI applications. For Anyscale that means “this is a much bigger market than just machine learning experts,” he said. Currently Ray is an open-source platform that’s free to use, and Nishihara estimates there are tens of thousands of companies using Ray. As for Nishihara, “we’re all busier now,” he says of his day-to-day life in recent months.

Baidu

Baidu, a Google clone that has a market capitalization of almost $38 billion on the New York Stock Exchange as of November 2023. While best known for its Chinese-language-optimized search engine, it also has its tendrils in a suite of other technologies, especially artificial intelligence. Baidu has trained a ChatGPT competitor, ERNIE bot, named after the famed Muppet on Sesame Street. (In an inside joke among AI developers, many models borrow their names from the Muppets.) In October 2023, the firm released ERNIE 4.0 and claims that it outperforms OpenAI’s chatbot in a number of Chinese-language-specific tasks and matches ChatGPT in sophistication and capabilities. And, in addition to using machine learning in its search engine, cloud computing arm, and other products, Baidu is developing autonomous driving algorithms and has a fleet of driverless “robotaxis” that navigate the busy streets of Beijing and three other Chinese cities.

Bloomberg

The financial information giant launched BloombergGPT in March for research purposes. It has 700 million tokens, although currently only 600 billion tokens were used to train the model. BloombergGPT outperformed similar AI tools in both finance-specific and general language comprehension tasks, according to the company. With more than half of the data used to train it coming from proprietary information, BloombergGPT could offer a future template for corporate uses of AI. The company is already a leader in using natural language processing to help users of its financial data and news services find the information they need and to gain trading insights such as sentiment analysis. It also a pioneer in using AI to write headlines and corporate earnings stories.

C3.ai

AI is now all the rage. But C3 AI began positioning itself in the market over a decade ago. In March, C3 AI released C3 Generative AI, making it one of the first companies to offer a generative AI solution that ran on an enterprise’s own information systems. C3 Generative AI projects are now in place at Georgia-Pacific, Flint Hills Resources, Nucor, Pantaleon, Con Edison, and multiple U.S. Department of Defense agencies including the U.S. Air Force and the Missile Defense Agency. Using the technology, the Missile Defense Agency was able to reduce flight test analysis and report time from one to two months to one week, the company says. More broadly, the Redwood City, Calif.–based company, run by billionaire enterprise software guru Tom Siebel, has provided AI tools for sectors that include manufacturing, financial services, and oil and gas. It’s involved in some of the largest energy optimization and predictive maintenance projects with companies including European utility Enel, Duke Energy, and Shell plc. Founded in 2009, the company went public in December 2020. C3 AI’s total revenue for fiscal year 2023 was $266.8 million, an increase of 5.6% compared to its FY ’22.

Cerebras

Cerebras’s flagship computer chip is the size of a “dinner plate,” cofounder and CEO Andrew Feldman says. It’s the largest chip ever built, he claims. That huge chip is designed to make it easier to run today’s massive AI models without having to worry about splitting the workload among many different GPUs. Since its founding in 2016, Cerebras has moved beyond chip-making to make its mark throughout the AI pipeline, from developing its own custom servers to now its own open-source AI models and datasets. Along the way, the company has accrued about $720 million from private investors at a valuation over $4 billion as of its last funding round in November 2021. Almost two years later, in July 2023, the company unveiled Condor Galaxy, a network of nine “supercomputers” powered by those dinner plate-size computer chips, and among its customers it counts COVID vaccine developer AstraZeneca and the Pittsburgh Supercomputer Center. While Cerebras often touts the size of its chip and data centers, CEO Feldman says his company is not done growing yet: “We’re building bigger and faster supercomputers to enable customers to do this work faster.”

Character.AI

What if you could talk to Elon Musk? Or have a conversation with Draco Malfoy from Harry Potter? Character.AI lets users chat with everyone from billionaires and celebrities to historical and fictional characters. The online generative AI chatbot uses deep learning algorithms and large language models to engage the user in conversation the way the character it’s mimicking would in real life. Character.AI was founded in 2021 by ex-Google engineers Noam Shazeer and Daniel De Freitas, who serve as CEO and president of the startup, respectively. Earlier this year, it raised a $150 million Series A led by Andreessen Horowitz at a $1 billion valuation. The startup is reportedly in talks to raise more VC funding at a $5 billion valuation, including from their former employer Google, per Reuters. Character.AI is free to use, but users can pay a $9 monthly subscription to skip the virtual queue to talk to characters. The company said its website attracted 100 million monthly visits in the first six months after its launch.

Cohere

While it doesn’t have as high a profile as competiotrs OpenAI and Anthropic, Cohere, an AI model developer created by Google Brain alumni, aims to be the AI platform for enterprises. Cohere builds large language models that companies can use to incorporate AI into things like copywriting and search and summarization of texts and web pages. The four-year-old startup’s products have caught on with a host of companies, including Spotify, Oracle, and Jasper. But feeding sensitive data into large language models has sparked a broader concern over privacy. To prevent companies’ proprietary data getting in the wrong hands, Cohere says, it provides services directly to the companies—be that with its existing cloud provider or on-site—so they can control their own data. In June, Cohere announced it had secured a $270 million Series C funding round from investors including Index Ventures as well as mega tech, software, and chip players Nvidia, Oracle, and Salesforce. Earlier this year the company also launched its enterprise AI assistant, Coral. “It’s a big challenge to stay ahead of the curve in terms of our models,” Aidan Gomez, Cohere’s cofounder and CEO, told Fortune in a note, but “it’s an exciting time to be working in the space.”

Conjecture

Connor Leahy, the cofounder and CEO of Conjecture, has an emerged as one of the leading voices calling for strict limits on building ever more capable “frontier” models because he feels AI poses an existential risk to humanity. But, in the meantime, London-based Conjecture is racing to figure out how to control large AI models and building some fairly capable AI systems of its own. Conjecture is a fairly new entrant in the AI race, having been established in March 2022. The company is backed by a group of investors including former Github CEO Nat Friedman and former senior AI director at Tesla Daniel Gross. Its founders, who in addition to Leahy include Sid Black and Gabriel Alfour, come from dynamic backgrounds—Leahy reverse-engineered GPT-2 and cofounded AI research lab EleutherAI with Black, while Alfour has founded two blockchain startups. Conjecture believes that existing large language models are black boxes that humans have little control over beyond the data they provide it with, and wants to offer an alternative to this approach which make systems explainable, bounded, and reliable. The AI company has raised $25 million so far.

Databricks

The San Francisco–based enterprise software company has been around for a decade, but is now putting its AI front and center. The company created its own bargain basement generative AI chatbot—Dolly—which cost only $30 to develop. Dolly 2.0 is an open-source model, which means that an organization can commercially use the same training sets and data the company did to build the chatbot. The hope is that others will be inspired to build their own generative AI tech. Dolly doesn’t have accuracy or the breadth of capabilities that ChatGPT does. But the point was to show that for a basic, no-frills chatbot, a company didn’t need to spend big bucks and have tons of data. In May 2023, the company surveyed over 9,000 of its customers about how they were adopting AI and found that there was increasing demand for the company’s data and AI platform, Databricks Lakehouse. A month later it acquired MosaicML, a innovative platform that lets users create their own generative AI models, in a deal valued at $1.3 billion. Databricks also garnered an additional $500 million in Series I venture capital funding in September with Nvidia as a new strategic investor.

Eleuther AI

In May 2020, OpenAI released a research paper that detailed how the larger an AI language model is, the more capable it is. “The limit, as far as we’re aware, is the amount of money you’re willing to spend,” Stella Biderman, executive director of EleutherAI, said. In tandem, OpenAI restricted access to ChatGPT-3 only to approved researchers. Some were frustrated, and on a Discord server, they banded together in a collective eventually named EleutherAI to try to replicate the scale of OpenAI’s achievement. “Society has mechanisms for interacting with technology that are very hard to actually carry out if the technology is locked up behind closed doors,” Biderman said. The group, a hodgepodge assortment of computer science, philosophy, and English majors, soon released a series of large language models. And in early 2022, it unveiled GPT-NeoX-20B, which, at the time, was the largest large language model ever to be made publicly available. The research collective has since pivoted from building large, open-source models to other areas of AI research, including studies delving into the limitations and risks of AI. “Nowadays, there’s a lot more companies that are interested in providing open-source models,” Biderman said of EleutherAI’s impact.

Eleven Labs

“You have a movie where there is one voice that will speak over all the scenes and all the voices to deliver the content in the Polish language. As you can imagine, it’s a pretty bad experience,” noted ElevenLabs cofounder Mati Staniszewski, who experienced this problem alongside his best friend and cofounder, Piotr Dabkowski. Combining their experience in machine learning at Palantir and Google, they debuted AI-powered speech software in January 2023 that turns text into spoken words. Users can design their own AI voices, but what really caught people’s attention was how good their software is at cloning people’s voices from short audio samples. While ElevenLabs’ terms and conditions state that people must have permission to replicate others’ voices, the software attracted attention after people used it to create unauthorized audio deepfakes of celebrities such as Ben Shapiro and Emma Watson saying offensive things and it is beleived that criminals may have used the software to help perpetrate a series of scams. “That’s something that we don’t support, and we’ll always take action,” Staniszewski said, and added that all audio files on the platform are traceable. Other safeguards have been put in place to verify users and make sure the voices they are cloning are their own. ElevenLabs insists that its software’s real killer use case will be to help creators, corporations, and audiobook publishers expand the geographic reach of their content, enabling someone to translate spoken words into over 20 languages without distorting the original voice. They have reported hundreds of thousands of paid registrations but declined to provide revenue. It raised $19 million in a June 2023 Series A, led by former GitHub CEO Nat Friedman, Daniel Gross, and Andreessen Horowitz.

EvenUp

Lawyers have tedious jobs, and sifting through paperwork can take hours. For those who specialize in personal injury claims, it can take months for their clients to get paid, if at all. EvenUp, a four-year-old startup, aims to slash that time and get clients higher settlements. The buzzy startup has caught the eye of top Silicon Valley investors—and the AI zeitgeist—raising nearly $65 million from venture firms including Bessemer Venture Partners and Bain Capital Ventures in reportedly highly competitive rounds (EvenUp reportedly raised even more since). EvenUp CEO Rami Karabibar, claimed customers saw a 30% increase in payouts, plus time savings, he told Reuters in June. So far, the startup has about 500 customers—only a fraction of what Karabibar estimates is the pool of 300,000 personal injury attorneys who could use its product. EvenUp users pay a sum—ranging from thousands to hundreds of thousands of dollars—for an annual subscription, and Karabibar told Reuters the firm has more than $10 million in recurring revenue in 2023. Though other startups are going after the AI-for-lawyers space, investors like that EvenUp is focused on a specific niche. “By not trying to be everything to everyone in the expansive legal services landscape, they are laser-focused on creating tremendous value for their customers,” Sarah Hinkfuss, partner at Bain Capital Ventures, who invested in EvenUp, told Fortune.

Exscientia

To date, no drug discovered through an AI-led process has made it past Phase II human clinical trials. Exscientia is among the companies working hard to change that. Founded in 2012, the company, based in Oxford, England, has brought six drugs that it discovered with the help of AI into clinical trials. (A Japanese pharmaceutical company now holds exclusive rights to three of those.) Its current portfolio runs the gamut from anticancer meds to anti-inflammatory molecules, and the firm has raised serious cash, going public in October 2021 at a valuation of almost $3 billion. (Its market capitalization is approximately $680 million as of October 2023.) Andrew Hopkins, Exscientia’s founder and CEO and a longtime veteran of the pharmaceutical industry, says his AI-powered drug-discovery process significantly reduces the time needed to find promising molecules compared with legacy firms. “It really is a huge reduction by allowing the use of generative AI with our expert chemists to really help them in those design decisions,” he said.

Google DeepMind

Big Tech has been salivating over recent innovations in artificial intelligence, but Google has long been a pioneer in AI. Google Research as well as DeepMind, which was acquired by Google in 2014 and recently merged with its sister AI research organization to become Google DeepMind, have been responsible for some of the most significant AI breakthroughs in the past decade including: AlphaGo, the first computer program to defeat a professional human player of the board game Go; AlphaFold, an AI system that predicts protein structure; and generative AI chatbot Sparrow. And in 2017, Google Research invented the Transformer, a neural network design that is the underlying technology in most of today’s generative AI products. Google has also been working to infuse AI into all its products, including its Workspace office productivity software. It launched Bard, a chatbot, based on its powerful PaLM 2 large language model, that is Google’s answer to OpenAI’s ChatGPT and Microsoft’s OpenAI-powered Bing. It is also preparing to unveil a next generation AI model called Gemini that it has teased is more capable than any AI modeled yet unveiled by any competitor. It is believed the company is probably working hard on building AI systems that will act as agents for users across the internet, doing everything from booking flights and restaurants, to doing your online grocery shop for you. Google has also been trying to use its generative AI chops to lure more big business customers to its Google Cloud Platform. Some industry watchers argue Google’s wealth of data and deep AI bench will work to its advantage, allowing it to fend off any significant challenge from Microsoft and OpenAI. Plus, the Big Tech giant isn’t only focused on its own AI projects: Google reportedly invested $300 million in AI model developer Anthropic, which is also on Fortune’s list. Still, there’s no getting around the fact that many generative AI use cases pose a long-term challenge to Google’s business model, which is based primarily around advertising, not software-as-a-service subscription models which seem more applicable to a world where users no longer go out looking for stuff on the internet but instead depend on AI agents to bring us stuff they find for us. You can’t monetize AI eyeballs like real ones.

Hippocratic AI

Hippocratic AI cofounder and CEO Munjal Shah envisions a world with 10 times as many nurses as we have today. They could call you to read lab results, help manage chronic condition care, and answer your questions—all in your preferred language. Except it would be an AI nurse, trained on a health-care-specific large language model. And it could cost as little as five cents an hour to deliver that care.The startup’s large founding team includes physicians, the former COO of a major hospital, and a coauthor of Google’s medical LLM, Med-PaLM.

Hippocratic AI launched in May 2023 with a $50 million seed round led by General Catalyst and Andreessen Horowitz. Shah said products won’t hit the market until they have achieved accuracy benchmarks and have the necessary guardrails in place. At that point, it will be partnering with health care systems to implement the products.

Hugging Face

There’s no bigger player in the market for open-source AI models than Hugging Face, which has become a must-stop shop for AI developers hunting for models and tools they can easily use to build AI-powered stuff without having to pony up huge fees to OpenAI, Anthropic, or Google. The company was founded in 2016 by three entrepreneurs who were originally developing a fun chatbot for the iPhone (the name was inspired by the so-called Hugging Face emoji). But enthusiasm from the AI community saw the company change its focus, becoming a platform that helps AI developers with find models, datasets, and tools. It is also become the go-to distribution platform for anyone else who wants to put an open-source AI model or dataset out into the world, much as GitHub is for more conventional code. Hugging Face also builds some open-source AI models of its own, with its LLM BLOOM being the most well-known. The firm’s open-source play has certainly paid off: Hugging Face reached a $4.5 billion valuation in August 2023 after a $235 million Series D round led by Salesforce Ventures.

IBM

Big Blue, based in Armonk, N.Y., was early to the AI game with Watson, which was first introduced over two decades ago, mesmerizing the world with what the tech could do. In 2023, the company launched its generative AI offerings, called Watsonx, with the conviction that the technology will bring about a productivity boom. CEO Arvind Krishna expects close to a third of current roles at the company can be taken over by AI and automation in the next five years, freeing up human staff for high-value work. The benefits from boosting productivity could mean reinvestment and larger margins, Krishna suggested. For its part, IBM’s Watsonx already has clients like NASA and Wix on board. The company also brings AI to clients to assist them with automation, modernization, and providing customer care services. For instance, Brazilian bank Bradesco automated its customer service answers using Watson Assistant, responding to 283,000 questions monthly. IBM is focusing on expanding the use of its AI where it is scalable and relevant, while strengthening its position beyond a legacy IT business; it has committed to training 2 million people in AI over the next three years. It acquired software company Apptio in a $4.6 billion deal to complement its AI portfolio and Red Hat cloud business.

Inflection

This startup may be young and under the radar to many, but it’s not to be underestimated. It has a heavy-hitting founding team, including Google DeepMind cofounder Mustafa Suleyman, former DeepMind principal scientist Karén Simonyan, and LinkedIn cofounder and venture capitalist Reid Hoffman, Inflection has raised more than $1.5 billion in funding so far from investors like Microsoft and Nvidia. The company released conversational generative AI chatbot Pi, which is designed to be an emotionally supportive conversationalist and can be integrated into iMessage and other communication platforms. “Pi is a new kind of AI, one that isn’t just smart but also has a good EQ. We think of Pi as a digital companion on hand whenever you want to learn something new, when you need a sounding board to talk about your day, or just pass the time with a curious and kind counterpart,” said Suleyman when the chatbot launched. What is notable about Pi though is what is under the hood, Inflection’s Inflection-1 LLM, which can rival those created by OpenAI and Anthropic on some tasks. And Suleyman has long hinted that he sees the future of the company in the race to create not just an EQ-enabled chatbot, but an AI-powered, personal “chief of staff” that will help users organize their work and personal lives and perform myriad tasks on their behalf. In July, the company joined Amazon, Microsoft, OpenAI, Meta, and other AI labs alongside the White House to commit to establishing safe AI measures.

Intuit

When it comes to developing useful AI systems, data is everything. And financial software giant Intuit, maker of TurboTax, QuickBooks, Credit Karma, and Mailchimp, has a lot of it. Earlier this year, the company announced GenOS, its proprietary operating system for generative AI development that works with best-in-class third-party large language models (LLMs), as well as Intuit’s own custom-trained financial LLMs, fine-tuned to solve tax, accounting, cash flow, personal finance, and marketing challenges. The company works with over 24,000 financial institutions, and they generate 65 billion machine learning predictions per day. “We don’t have a group on the side working on AI. It is core to everything that we design,” Intuit CEO Sasan Goodarzi said at a Fortune conference last year. The company continues to incorporate AI into its human financial expert offerings in its TurboTax Live and QuickBooks Live products, and in September released Intuit Assist—the company’s generative-AI-powered financial assistant that works across all Intuit products.

Jasper AI

Jasper AI first got onto venture capital firm IVP’s radar when an internal sourcing tool flagged Jasper’s site as among the top 1% of prospects out there. “Jasper was one of the only software tools I’ve ever heard people refer to as a person,” says Karthik Ramakrishnan, who led IVP’s investment in Jasper. “People would actually say, ‘He helped me come up with a blog post,’ or ‘He helped me deliver my campaign much faster.’”

Created by former marketers and best friends Dave Rogenmoser, J.P. Morgan, and Chris Hull to help other marketers develop advertising campaigns, Jasper has built an extremely loyal following among its 100,000 paying customers. The platform enables users to write copy, create images, and even select different brand voices. The challenge now is to simplify all those features and make it easier for users to find them all, says Rogenmoser. Originally built on OpenAI’s tech, Jasper has now started to build its own models, especially as it looks to deliver more bespoke offerings to its enterprise customers.

Despite being founded in early 2021, Jasper has already achieved significant financing milestones, having raised $125 million in a Series A from the likes of Bessemer Venture Partners and HubSpot Ventures, and has even reached unicorn status with a $1.5 billion valuation. This has led Jasper to grow its headcount from nine people to more than 200 over the past year and a half, according to Rogenmoser.

LAION

Christoph Schuhmann is an unassuming high school teacher in Hamburg. He is also a cofounder of one of the most influential nonprofits in the field of AI. In 2021, Schuhmann and other part-time AI researchers established LAION, short for “Large-scale Artificial Intelligence Open Network.” In a field dominated by tech behemoths like OpenAI and Google, their nonprofit wants to make AI open-source, or freely available to researchers like them. And the team has had unprecedented success. Stability AI, the company behind popular image generator Stable Diffusion, trained its model on a dataset of billions of image-to-text pairs that Schuhmann curated between the physics and computer science classes he teaches every weekday. Google, Meta, and Microsoft have also used LAION’s datasets to train their own AI algorithms, Schuhmann says. And his nonprofit is now training its own open-source models. “We really try to democratize not only datasets, but also models and code,” says Robert Kaczmarczyk, a cofounder of LAION and a doctor in Munich. But LAION is also controversial. Its datasets contain tens of thousands of copyrighted works. Under EU law, non-commerical entities such as LAION allowed to use copyrighted material for data mining. But artists and copyright holders say that LAION engages in “data laundering” and that it violates the spirit of the EU data mining law when it then sells or makes its datasets available to for-profit partners, such as Stability and others.

LangChain

Vanilla ChatGPT is, well, sort of vanilla. Its ability to generate almost any style of writing is impressive, but, until recently, it didn’t have access to Wikipedia, couldn’t report today’s weather, and didn’t have analyses of the Supreme Court’s most recent decisions. Developers, however, can load outside information into the chatbot to make its often subpar responses remarkable. And while this can be technically difficult for a newbie who wants to automate the process, LangChain has developed comparatively easy-to-use, open-source tools to fully exploit the power of large language models. It allows developers to chain prompts together, save prompts, and gives AI models easy ways to access external databases. Many of these features were so popular that OpenAI has essentially tried to copy them with its new GPT tools. But, especially for those new to developing AI applications and for those who want to use models other than OpenAI’s GPTs, LangChain gives programmers easy ways to build AI applications. Chase and Gola’s open-source project has attracted a fleet of developers and inevitably venture capital. Created by founders Harrison Chase and Ankush Gola in October 2022, to date LangChain has raised at least $30 million from Benchmark and Sequoia, and their last round valued LangChain at at least $200 million.

Meta

The social media conglomerate may not be as synonymous with the generative AI revolution as OpenAI, Microsoft, or Google, but the firm’s AI research lab boasts some of the world’s top deep learning talent and its pioneering work on large language models has played a key role behind the scenes in Meta’s own products, from helping to moderate content on Facebook to helping to match advertising recommendations with users on Instagram. Now the company is also playing a critical role in the open-source generative AI world, releasing LLaMA (Large Language Model Meta AI), an open-source language model meant to match many of the capabilities of OpenAI’s ChatGPT and Google’s Bard, for free. Five months later, Meta released open-source Llama 2 with Microsoft, free of charge for commercial use or research. Yann LeCun, chief AI scientist at Meta, is a preeminent AI researcher and one of the “godfathers of AI.” He’s been increasingly vocal in arguing against heavy-handed AI regulation, especially rules that might make it harder for open-souce AI. He has also emerged as a leading critic of the idea that AI could pose an existential risk to humanity, a position that has pitted him against his fellow deep learning godfathers, Geoff Hinton and Yoshua Bengio. In June, Meta announced Voicebox, a generative AI voice model that can produce high-quality text-to-speech with audio samples as short as two seconds long, among other services. Most recently, Meta announced Habitat 3.0, a simulator for AI models that it hopes to use to train socially intelligent AI assistants in the form of physical robots. It has also launched a number of AI chatbots with distinct communication styles licensed from celebrities from Paris Hilton to Snoop Dogg.

Microsoft

While OpenAI deserves the credit for having created ChatGPT, it wouldn’t have happened without billions of dollars in investment from Microsoft. The tech giant has committed a reported $13 billion to OpenAI so far and it has built one of the world’s largest supercomputing clusters to help OpenAI train its ever-larger, more capable AI models. Microsoft’s Bing Chat, a chatbot and search engine, was built with models from OpenAI, and since its debut, users engaged in over a billion chats with the bot and generated more than a billion images with Bing Image Creator, which uses AI to turn user prompts into images. What Bing hasn’t done, however, is move the needle on Microsoft’s search business: Bing remains stuck at about 3% of the global search market, compared to Google’s 91%. But for Microsoft, the more important wins from generative AI have been in its Cloud business. Here, Microsoft’s ability to offer OpenAI’s technology to its Azure cloud customers has helped the tech giant turns in sales and profit growth that has beaten Wall Street expectations, with generative AI accounting for about 3% of the growth in Cloud revenues in the quarter ended in September. Microsoft has also been implementing AI across its core business producitivity software products, from PowerPoint to Outlook, as well as adding products like GitHub Copilot, an AI-powered coding assistant.

Midjourney

What would the Pope look like in a trendy, white puffer jacket? Or Donald Trump getting arrested? Thanks to Midjourney, you don’t have to picture it in your head—you can make it a reality in minutes. The less-than-two-year-old research lab, based in San Francisco, is behind some of the most viral AI-generated photos over the past year as the creator of its eponymous and wildly popular text-to-image generation system. Midjourney lets users create highly photorealistic images based on text prompts. But it’s also at the center of several controversies around the murky new world of AI-generated photos—including criticism over the way fake but realistic-looking photos of Trump and other figures could be used for political disinformation, as well as backlash from artists over the use of copyrighted material to train Midjourney and concerns that it is reducing both the amount of paid commercial illustration and photography work available as well as the amount businesses will pay artists and photographers for imagery. Many photographers were up in arms when a Midjourney-created image took a prestgious photographic prize earlier this year. The lab has also been under fire for its moderation standards, which some have criticized as inconsistent. Founder and CEO David Holz said earlier this year: “We’re taking lots of feedback and ideas from experts and the community and are trying to be really thoughtful.” The research lab has made big strides in the text-to-image generated space in its short existence, and its “goal is to make humans more imaginative, not make imaginative machines,” Holz said last year. Interestingly, the lab has so far eschewed venture funding.

Nvidia

Nvidia may have been founded in 1993, but no company has capitalized the AI boom with the success that the chipmaker has. Nvidia is the reigning monarch of AI hardware. Its graphics processing unit (GPU) chips are essential for the training of most top AI models (Google’s models are the exception; the search giant mostly uses its own chips in its datacenters.) As the essential AI company, Nvidia’s stock has gained an eye-popping 289% year to date as of Nov. 2, 2023. Nvidia’s popular and well-supported CUDA software system, which makes it relatively easy for developers to program the company’s GPUs and its chip performance has enabled it to amass market share while other chipmakers chase its lead. While chips may be what Nvidia is known for, the company has been investing in its AI software for almost two decades, spending $30 billion in R&D in the past decade alone. And the company is increasingly starting to offer its own AI models and AI cloud services directly to enterprise customers, putting it into direct competition with some of its best customers, the giant “hyperscale” cloud providers such as Microsoft Azure. Nvidia launched two large language model cloud services for both AI and biotech that can help developers use LLMs for content creation.

OpenAI

OpenAI rocked the world with the launch of ChatGPT in November of 2022—and then again in mid-November when the abrupt ouster of cofounder Sam Altman captivated the business and tech worlds over an insane weekend of boardroom drama. It’s not remotely clear what the fallout from the OpenAI combustion may eventually be. And, as Fortune has reported, it was the unusual corporate structure that OpenAI was built upon that enabled the power struggle to play out in the first place. Founded in 2015 as a nonprofit (the company has since added a capped-profit arm) by a group of tech entrepreneurs including Elon Musk (who has since broken with the company) and now ex-CEO Altman, OpenAI is among those in the AI space whose explicit goal is the creation of artificial general intelligence (AGI), a single AI model that can perform any economically-valuable intellectual task humans can as well or better than we can. It took only two months for ChatGPT to reportedly garner 100 million monthly active users, making it the fastest-growing consumer application ever at the time. OpenAI’s latest version, GPT-4 Turbo, which users of OpenAI’s paid ChatGPT Plus service and enterprise customers of its API have access to, is the most capable general-purpose large language foundation model yet released. It can do everything from write code to write plays, as well as create images, give you recipes based on a photo of what is in your fridge, and use an increasing number of internet-connected tools. A version of GPT-4 is also integrated with Microsoft’s search engine, Bing. Its massive ambitions have earned OpenAI equally massive funding—including $13 billion from Microsoft alone. But the company’s innovations have also put it under scrutiny, including from the government, over what its technologies could do and what regulations should govern—and control—it. The company is also facing numerous lawsuits for copyright infringement and data privacy leaks. Whatever ends up happening with Altman and the rest of the OpenAI team, you can bet on this, the world will be watching.

Palantir

The giant data mining and software company whose largest clients include many governments, militaries, and intelligence agencies, was established in 2003 with PayPal’s Peter Thiel among its founders. Palantir has been working on AI for a long time; last year, its tech tools were used by Ukrainian forces in the war with Russia. The recent generative AI frenzy has created a surge in demand for the Denver-based company’s tech. In April 2023 the company unveiled its artificial intelligence platform (AIP), which uses AI to analyze data in different real-world scenarios. Earlier this year, CEO and cofounder Alex Karp said AI tools represented an “infinite market.” Palantir’s share price doubled in the first six months of 2023 as a result of booming demand for its platform. The company is optimistic about the coming months, as it forecast a profit for every quarter of the year in May. Despite the massive power and potential of Palantir’s AI tools, Karp has maintained that the tech will continue to remain a “subordinate” to its creator rather than becoming a powerful force on its own. Like many other tech companies, Palantir carried out a series of cost-cutting measures, including laying off about 2% of its workforce and reducing its cloud expenditure.

PathAI

Modern medicine has advanced at a rapid clip in recent decades, but some crucial parts of the job, like pathology, where doctors examine cells to diagnose diseases like cancer, are still up to the sometimes-flawed human eye. That’s where AI can come in handy. Boston-based PathAI uses machine learning and AI algorithms to help pathologists and researchers analyze images of cells more accurately and efficiently, helping them also discover new biomarkers to aid diagnosis and future drug development. The company develops algorithms trained on proprietary, crowd-sourced data from over 450 pathologists. More than 45 pharma and biotech companies, 3,500 providers, and 50 labs are using PathAI’s technology, according to the company. PathAI primarily works with doctors treating cancers, liver diseases, and bowel syndromes. CEO Andy Beck, a board-certified pathologist himself, predicts that in 10 years pathologists are “not going to be counting individual cells like they are today or doing measurements manually. All of that low-level work will be done, and the AI system will provide specific recommendations about, ‘This is the diagnosis, this is the recommended care path,’” he told Fortune. PathAI’s platform is already being used to develop new drugs, which are in all phases of clinical trials, Beck told Fortune. Investors including General Atlantic and D1 Capital Partners also see the promise in PathAI’s technology, giving the startup a total of over $350 million in funding. It recently announced new products including Nash Explore and IBM Explore, which use AI to categorize eight types of cancers and markers that indicate ulcerative colitis, respectively.

Perplexity

The San Francisco–based Perplexity is building a generative AI chat-based search engine that could rival Google search and Microsoft’s Bing. It was founded in 2022 by Aravind Srinivas, Denis Yarats, Johnny Ho, and Andy Konwinski, who have experience in AI and machine-learning-based roles at tech companies. The company reported having 10 million monthly visits and 2 million unique visitors in just February. The company’s interface is more like a chat screen, and Perplexity claims it is far more accurate in its answers and less prone to hallucination than some other chatbot search engines out there. Perplexity launched its platform on Apple’s iOS in March and within six days had hit over 100,000 downloads. What sets Perplexity apart, according to Srinivas, is its ability to balance between ranking search results and using large language models to produce summarized, capsule answers that cites its sources and that also allows users to ask follow-up questions. The company plans to monetize its business by offering paid features. It faces lots of competition in the high-demand AI search space, but Perplexity has set itself apart by catching the attention of AI veterans like Turing Award winner Yann LeCun and Google AI research head Jeff Dean. It has raised over $28 million so far and is in talks with Instacart and Klarna about integrating conversational search into their platforms.

Pinecone

Humans are not all-knowing, but we are able to search through textbooks, online databases, and encyclopedias to find out an almost limitless amount of information. Similarly, when users ask for information not hard coded into an AI model, the model needs to do its own research. This is where Pinecone and vector databases come into play. For every AI application worth its salt, founder and CEO Edo Liberty says, is an accompanying database it can ping to get information on, for example, the current price of Apple’s stock or nonpublic data on a bank’s roster of customers. Pinecone develops the infrastructure for companies big and small, including Microsoft and CVS, to create their own vector databases to give their AI models what it calls “l(fā)ong-term memory.” In its first year of operations after launching in early 2022, Pinecone grew its customer base from zero to 170 and generated more than $2 million in sales, Liberty says. And in April 2023 it raised $100 million at a valuation of $750 million, bringing the total amount of funds the San Francisco–based startup raised to $138 million, according to Crunchbase.

Profluent

Finding the chemical code that can cure a disease is like finding a needle in a haystack, says Profluent CEO Ali Madani. In the same way that large language models allow ChatGPT to predict the correct answer to your question, Madani says similarly-designed AI models might be able to “l(fā)earn the underlying language of nature to go solve the most challenging problems in human health and the environment.” He spent several years researching this question alongside scientists at UCSF, Stanford, and in the industry to test whether AI-designed proteins would be useful. The technology that they invented was able to create functioning proteins, as detailed in a peer-reviewed paper in Nature Biotech. These AI-designed proteins can be used in drug discovery and across other biotech functions, a prospect that inspired Madani to start Profluent. Madani is a solo founder with a team of scientists, technologists, and entrepreneurs across biology and machine learning fields. That team is poised to grow after launching in January 2023 with a $9 million seed round led by Insight Partners. “Even with all the recent AI-driven breakthroughs in protein structure prediction, Profluent’s work stands out,” said Dylan Morris, managing director at Insight Partners.

Replika

“We’re in the worst crisis of loneliness,” says Eugenia Kuyda, the founder and CEO of Replika. Kuyda, a former journalist, hopes to combat loneliness with Replika, her AI-companion startup. Founded in 2017, her company gives users a taste of the dystopian (or utopian, depending on one’s perspective) movie Her, in which actor Joaquin Phoenix falls in love with his AI assistant. Replika users can create their own virtual friends, customize their appearances, and chat with them at length. As of October 2023, the application has about 2 million monthly users and 250,000 paid subscribers. It has raised almost $11 million from private investors, according to Crunchbase. This funding is a pittance compared to that of AI juggernauts like OpenAI. But Replika’s pitch of virtual friendship has attracted substantial media buzz, especially coverage of its users’ propensity to turn their AI friends into AI friends with benefits. In February 2023 the company removed the ability for users to engage in erotic roleplay, but after community outcry, it reversed course and allowed those with accounts created before February 2023 to continue to dirty-talk their chatbots. Replika has also launched Blush, an app specifically designed for users who want to “date” an AI chatbot. Kudya says that just as there was initially a stigma around online dating that eventually faded completely, she thinks today’s stigma around having a romantic relationship with an AI bot will also fade in time.

Runway

Making a movie has historically been a time- and capital-intensive process that a novice videomaker couldn’t dream of successfully embarking on solo, but the founders of startup Runway are changing that. The company, which is known for helping to build popular text-to-image model Stable Diffusion, recently launched the first publicly available text-to-video platform, Gen-2. The company currently offers over 35 tools for AI-powered video editing to production companies, news publications, or individual content creators. Runway also allows creatives like filmmakers and artists to generate entire shots and scenes, and the tech was notably used to edit parts of movies including Everything Everywhere All at Once. Some of its customers include New Balance, CBS, and Vox, according to a spokesperson from the company. In June 2023 Runway raised $141 million in Series C extension funding at a $1.5 billion valuation with participation from Google, Nvidia, and Salesforce Ventures. The company recently released new research detailing ways to reduce bias in models that generate images from text and unveiled Director Mode, which gives users more control over scene generation.

Salesforce

The cloud-based customer relationship management platform has been using AI since 2016 under the umbrella of its AI platform Einstein. To grow its offerings for customers and employees, Salesforce announced that Einstein GPT, launched in March 2023, would integrate with ChatGPT-maker OpenAI. Some key users of Einstein bots include Formula 1, which leverages the tech to offer personalized fan experiences, and Gucci, which has used it for customer-facing aspects of that business. Salesforce also operates a generative AI-focused venture capital arm through which it plans to invest $500 million in the tech. Security of data has been a top priority in Salesforce’s AI expansion endeavors so that large language models don’t accidentally leak customer data. The company emphasizes a “human in the loop” where generative AI is involved to prevent misuse and guard against AI hallucinations that could adversely impact customers. Clara Shih was appointed the head of AI in May 2023, before which she led Salesforce Service Cloud. The company is looking to AI to propel its growth and has introduced a suite of tools that help enterprises tap on the power of generative AI, such as AI Cloud. Benioff expects messaging platform Slack to be a key part of the AI push. Salesforce has also made deals such as the acquisition of California-based Airkit.ai and expanded its partnerships with AWS, Google, and Databricks to further bolster its strategy.

Scale AI

AI automates, but to get to the sophistication of chatbots like ChatGPT requires humans—and a lot of them. While the LLMs behind OpenAI’s ChatGPT or Anthropic’s Claude learn the statistical connections in language on their own, to guide and refine the responses these models will provide through a chatbot interface, companies turn to a process known as reinforcement learning from human feedback (or RLHF) where human evaluators judge whether the model’s answers are useful, helpful, and “safe” (usually meaning not offensive or likely to be used for harm). Then the model is rewarded for producing answers more like those that the human evaluators labelled as good. But all that requires humans. Which is where Scale comes in. Its whole business is around providing the data labels that AI companies need. Often, those labelers work for low wages in the developing world. In Kenya, for example, Scale AI, a Silicon Valley darling, paid workers between $1 and $3 to train AI models, according to The Verge. (A spokesperson said the company adheres to minimum wage law in all counties in which it operates.) Founded by Alexandr Wang in 2016, who was then only 19 years old, Scale is best known as the data maven behind many of the world’s most sophisticated AI models, including those used by a number of self-driving car companies and to support decision-making in defense. The tasks are often simple and monotonous: identify characteristics of retail products or identify objects in an image. While Scale got its start labeling objects in image data for self-driving algorithms, it has now expanded its training to a wide assortment of AI models for customers like Etsy, Instacart, Meta, and, of course, OpenAI. And it’s ridden the AI boom to raise a total of approximately $600 million at a most recent valuation of $7.3 billion. CTO Vijay Karunamurthy said, however, that Scale, which also builds its own AI algorithms, is looking to go beyond finding laypeople to more curated experts, like an authority in ancient Aramaic, to help train models: “Expert feedback is incredibly valuable to help the model structure its thinking.”

Snorkel AI

“Data is often overlooked,” says Alexander Ratner, cofounder and CEO of Snorkel AI. “It’s often treated like an upstream janitorial process.” Despite the lack of glamour in wading through the statistical muck, Ratner’s startup, which began as a research project in the Stanford AI Lab in 2015, has attracted enough attention from private investors to raise $135 million—with a $1 billion valuation as of its last funding round in August 2021. And its customers include brand names like Wayfair, BNY Mellon, and Memorial Sloan Kettering. At its core, Snorkel AI uses artificial intelligence to help train artificial intelligence. Every AI model learns from a dataset, which can often measure in the terabytes. To shape these datasets, Snorkel uses its own proprietary AI algorithms as well as humans. Experts like journalists, network technicians, or doctors teach Snorkel’s models how to label, clean up, and shape data. Through multiple rounds, a doctor, for example, will coach AI in how to better classify ultrasounds. Ratner says this speeds up the labeling and curating of data, in a process 10 to 100 times as fast as conventional techniques. “That inglorious messy janitorial data work [has been] our whole pitch over the last nine years,” he notes.

Stability AI

Stability AI is best known for its efforts in helping create the ultra-popular text-to-image generation system Stable Diffusion—where users can input a prompt and create an AI image of anything they want—but the startup is working on a host of other AI projects that are open models, meaning anyone can access them. This spring, Stability released two language models, StableLM and Stable Vicuna, as well as two additional imaging models, according to the company. It has also created a text-to-audio model called Stable Audio that it released in September. Stability has been an early and vocal champion of open-source AI, where people can access the entire model, all of its code and weights, as well as all of its outputs, for free and with almost no restrictions. Some big investors have gotten on board with the startup’s creed, as Stability has raised over $110 million, per PitchBook data, from the likes of Coatue Management and Lightspeed Venture Partners. Over 200,000 creators, developers, and researchers, as well as seven research hubs, use Stability’s products, according to the company. However, Stability and its founder Emad Mostaque have also recently come under scrutiny: Mostaque reportedly has a history of exaggeration, and a Forbes investigation found that several of his assertions about his own background and Stability’s customers were either false or inflated. Numerous senior executives and AI experts have reportedly left the company this year. However according to the company, overall headcount is up, and they increased their revenue base more than 10x this year. And the company got a boost in early November as Mostaque posted on X that “We closed strategic funding ourselves last month (announcements soon).” Bloomberg later reported that Stability had receieved a $50 million round of debt financing in a round led by Intel. But it also reported that both Coatue Management and Lightspeed had declined to provide the company further financing and given up theirs seats on the company’s board following disagreements with Mostaque over the company’s management and direction.

Sudowrite

“This might be controversial, but I don’t think everyday users of generative AI should use Sudowrite,” says Rishi Taparia, cofounder of Garuda Ventures, one of Sudowrite’s investors. Taparia’s sentiments, though, are more a testament to Sudowrite’s focus on serving professional writers rather than its limited market size. Sudowrite bills itself as the AI platform for creative writing. It has settings to help with screenplays and novels, and can perform specialized tasks like reviewing plot pacing and rewriting dialogue. The layout supplements generative AI’s usual words-on-a-screen presentation with visual elements like pictures of characters and an interactive timeline for outlines.

Fittingly, Sudowrite’s cofounders, Amit Gupta and James Yu, met in a writing group when they both took time off work after selling their previous companies. So far, the platform has 12,000 paying users, according to Gupta, with subscriptions ranging from $10 to $100 a month. Sudowrite has raised only $3 million to date, with no plans to raise additional funds at the moment, Gupta tells Fortune.

Synthesia

The AI video creation platform Synthesia has catapulted to unicorn status amid the AI craze. The company was founded in 2017 by a group of four entrepreneurs with experience at UCL, Stanford, TUM, and Cambridge; it uses AI to create video avatars primarily for instructional videos, saving enterprise clients time and money on production costs. As of June 2023 it has generated more than 12 million videos across 50,000 businesses, including almost half of the Fortune 100, which use Synthesia to create product marketing and educational videos in a matter of minutes. With 456% growth year over year, it is no wonder that it raised $90 million in a Series C round led by Accel with investment from Nvidia’s venture capital arm. “One of the most important philosophies of Synthesia is utility over novelty. And while we’ve seen a bump the last six months because of all the hype, I think this round and the interest of investors is very much a testament to the absolutely amazing business fundamentals that sit under that,” CEO and cofounder Victor Riparbelli told Bloomberg. Synthesia raised $90 million in Series C funding in June led by Nvidia’s NVentures at a $1 billion valuation. Controversially, some of the highly-realistic avatars the company has created, particularly a virtual news anchor called Jason as well as avatars called Darren and Gary, have been used for political disinformation campaigns in Mali, Burkina Faso, Venezuela, as well as advertising a cryptocurrency scam in California, according to a story in Wired. Riparbelli told Wired that the company has since made changes to try to prevent its avatars from being used in misinformation campaigns—including blocking the news-related scripts from its consumer-facing accounts and employing content moderators to review the scripts being fed to its avatars.

Together

AI needs villages-worth of computational power, and Amazon Web Services, Google Cloud, and Microsoft Azure are the cloud computing titans of choice. To challenge their dominance, Together “brings together” the little guys to provide AI researchers and developers comparable levels of computing power, which can be five times as cheap as what the giants of cloud computing charge, according to cofounder and CEO Vipul Ved Prakash. The startup, which is led by Stanford professors and tech industry veterans, announced $20 million in seed funding in May 2023. Together outsources a developer’s computational needs to alternative data centers—even former Bitcoin miners. And it also offers a comprehensive platform for AI development for both general developers as well as those who want to build their own models from scratch. “There’s already a fair bit of concentration with OpenAI and Google,” Prakash said. “So I think there is value in our more decentralized, less locked-in ecosystem.”

Tome

Imagine PowerPoint—but with an AI twist. That’s what Tome, a startup launched in September 2022, is pitching to investors and customers, and it’s already attracted more than 10 million users as of July 2023 and raised more than $81 million at a most recent valuation of $300 million. At its most basic, Tome, founded by two former Meta managers, uses large models like Stable Diffusion, ChatGPT, and even its own proprietary AI to generate custom presentations and imagery for users. But Keith Peiris, cofounder and CEO of the startup, says his vision goes beyond slideshow generation. “It’s sort of this wet clay that can become anything,” he said of Tome’s product. Founders can not only build a pitch deck to hawk their newest AI-powered tool, Peiris notes, they can build travel guides, résumés, or small websites. In the past, Tome relied on the vanilla text and images generated from its AI models, but a spokesperson said that, going forward, Tome is increasingly integrating outside data—like files in a user’s Google account—to further refine its AI-generated slideshows.

You.com

Before the recent AI craze took the world by storm, one company had already put AI chat search to the test. Cue: You.com. Its novel search engine uses generative AI for web search and content and image creation with a focus on user data privacy. You was founded in 2020 by Richard Socher, who used to be chief scientist at Salesforce with $20 million in seed funding led by Salesforce CEO Marc Benioff’s Time Ventures. It subsequently received $25 million in Series A funding led by Radical Ventures and further investment from Salesforce Ventures. The company’s domain had been unused and owned by Benioff since 1996. You.com‘s search bar appears similar to Google’s—but that’s about as far as the similarities go. Socher has previously called Google’s approach to privacy “pretty terrible.” The company has several other competitors in the search and privacy space such as DuckDuckGo, but You’s combination of data privacy and generative AI features sets it apart. In June 2023, the company introduced a paid subscription called YouPro to monetize its product, allowing unlimited access to users of its AI-powered chat and other generative AI features along with an ad-free experience for $9.99 a month. In September it launched an AI-enabled search function on WhatsApp, bringing its conversational search capabilities to one of the most widely used messaging platforms globally.